Integrating inverse data envelopment analysis and neural network to preserve relative efficiency values

[1]  Lawrence M. Seiford,et al.  Recent developments in dea : the mathematical programming approach to frontier analysis , 1990 .

[2]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[3]  Lawrence M. Seiford,et al.  Data envelopment analysis: The evolution of the state of the art (1978–1995) , 1996 .

[4]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[5]  Jianzhong Zhang,et al.  An inverse DEA model for inputs/outputs estimate , 2000, Eur. J. Oper. Res..

[6]  Hong Yan,et al.  DEA models for resource reallocation and production input/output estimation , 2002, Eur. J. Oper. Res..

[7]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[8]  George Halkos,et al.  Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach , 2004 .

[9]  Jin-Li Hu,et al.  The sources of bank productivity growth in China during 2002-2009: A disaggregation view , 2012 .

[10]  Daniel Santín,et al.  The measurement of technical efficiency: a neural network approach , 2004 .

[11]  Jie Wu,et al.  Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA , 2014 .

[12]  Shabnam Razavyan,et al.  The outputs estimation of a DMU according to improvement of its efficiency , 2004, Appl. Math. Comput..

[13]  M. Hassan,et al.  Technical, scale and allocative efficiencies of Turkish banking industry , 2002 .

[14]  Saeid Ghobadi,et al.  Inverse DEA under inter-temporal dependence using multiple-objective programming , 2015, Eur. J. Oper. Res..

[15]  Antreas D. Athanassopoulos,et al.  A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units , 1996 .

[16]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[17]  Shu-Cherng Fang,et al.  Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale , 2011, Comput. Ind. Eng..

[18]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[19]  Shabnam Razavyan,et al.  Input estimation and identification of extra inputs in inverse DEA models , 2004, Appl. Math. Comput..

[20]  Fotios Pasiouras,et al.  Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A Survey , 2009, Eur. J. Oper. Res..

[21]  Desheng Dash Wu,et al.  Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank , 2006, Expert Syst. Appl..

[22]  Raphael N. Markellos,et al.  Evaluating public transport efficiency with neural network models , 1997 .

[23]  John S. Liu,et al.  Data envelopment analysis 1978-2010: A citation-based literature survey , 2013 .

[24]  Allen N. Berger.  “Distribution-free” estimates of efficiency in the U.S. banking industry and tests of the standard distributional assumptions , 1993 .

[25]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[26]  Ali Emrouznejad,et al.  A combined neural network and DEA for measuring efficiency of large scale datasets , 2009, Comput. Ind. Eng..

[27]  L. Weill.  Measuring Cost Efficiency in European Banking: A Comparison of Frontier Techniques , 2004 .

[28]  Allen N. Berger,et al.  The dominance of inefficiencies over scale and product mix economies in banking , 1991 .

[29]  Dilay Çelebi,et al.  An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information , 2008, Expert Syst. Appl..

[30]  W. Cooper,et al.  Some models and measures for evaluating performances with DEA: past accomplishments and future prospects , 2007 .

[31]  Kweku-Muata Osei-Bryson,et al.  Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks , 2010, Eur. J. Oper. Res..

[32]  John S. Liu,et al.  A survey of DEA applications , 2013 .

[33]  Francisco J. Delgado.  Measuring efficiency with neural networks. An application to the public sector , 2005 .

[34]  Ali Asghar Foroughi,et al.  A generalized DEA model for inputs/outputs estimation , 2006, Math. Comput. Model..